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  <h1>Source code for torch.nn.modules.adaptive</h1><div class="highlight"><pre>
<span></span><span class="c1"># -*- coding: utf-8 -*-</span>

<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>

<span class="kn">import</span> <span class="nn">torch</span>

<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">ModuleList</span><span class="p">,</span> <span class="n">Linear</span>
<span class="kn">from</span> <span class="nn">.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">..functional</span> <span class="kn">import</span> <span class="n">log_softmax</span>


<span class="n">_ASMoutput</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">&#39;ASMoutput&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="s1">&#39;loss&#39;</span><span class="p">])</span>


<div class="viewcode-block" id="AdaptiveLogSoftmaxWithLoss"><a class="viewcode-back" href="../../../../nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss">[docs]</a><span class="k">class</span> <span class="nc">AdaptiveLogSoftmaxWithLoss</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Efficient softmax approximation as described in</span>
<span class="sd">    `Efficient softmax approximation for GPUs`_ by Edouard Grave, Armand Joulin,</span>
<span class="sd">    Moustapha Cissé, David Grangier, and Hervé Jégou.</span>

<span class="sd">    Adaptive softmax is an approximate strategy for training models with large</span>
<span class="sd">    output spaces. It is most effective when the label distribution is highly</span>
<span class="sd">    imbalanced, for example in natural language modelling, where the word</span>
<span class="sd">    frequency distribution approximately follows the `Zipf&#39;s law`_.</span>

<span class="sd">    Adaptive softmax partitions the labels into several clusters, according to</span>
<span class="sd">    their frequency. These clusters may contain different number of targets</span>
<span class="sd">    each.</span>
<span class="sd">    Additionally, clusters containing less frequent labels assign lower</span>
<span class="sd">    dimensional embeddings to those labels, which speeds up the computation.</span>
<span class="sd">    For each minibatch, only clusters for which at least one target is</span>
<span class="sd">    present are evaluated.</span>

<span class="sd">    The idea is that the clusters which are accessed frequently</span>
<span class="sd">    (like the first one, containing most frequent labels), should also be cheap</span>
<span class="sd">    to compute -- that is, contain a small number of assigned labels.</span>

<span class="sd">    We highly recommend taking a look at the original paper for more details.</span>

<span class="sd">    * :attr:`cutoffs` should be an ordered Sequence of integers sorted</span>
<span class="sd">      in the increasing order.</span>
<span class="sd">      It controls number of clusters and the partitioning of targets into</span>
<span class="sd">      clusters. For example setting ``cutoffs = [10, 100, 1000]``</span>
<span class="sd">      means that first `10` targets will be assigned</span>
<span class="sd">      to the &#39;head&#39; of the adaptive softmax, targets `11, 12, ..., 100` will be</span>
<span class="sd">      assigned to the first cluster, and targets `101, 102, ..., 1000` will be</span>
<span class="sd">      assigned to the second cluster, while targets</span>
<span class="sd">      `1001, 1002, ..., n_classes - 1` will be assigned</span>
<span class="sd">      to the last, third cluster.</span>

<span class="sd">    * :attr:`div_value` is used to compute the size of each additional cluster,</span>
<span class="sd">      which is given as</span>
<span class="sd">      :math:`\left\lfloor\frac{\texttt{in\_features}}{\texttt{div\_value}^{idx}}\right\rfloor`,</span>
<span class="sd">      where :math:`idx` is the cluster index (with clusters</span>
<span class="sd">      for less frequent words having larger indices,</span>
<span class="sd">      and indices starting from :math:`1`).</span>

<span class="sd">    * :attr:`head_bias` if set to True, adds a bias term to the &#39;head&#39; of the</span>
<span class="sd">      adaptive softmax. See paper for details. Set to False in the official</span>
<span class="sd">      implementation.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        Labels passed as inputs to this module should be sorted according to</span>
<span class="sd">        their frequency. This means that the most frequent label should be</span>
<span class="sd">        represented by the index `0`, and the least frequent</span>
<span class="sd">        label should be represented by the index `n_classes - 1`.</span>

<span class="sd">    .. note::</span>
<span class="sd">        This module returns a ``NamedTuple`` with ``output``</span>
<span class="sd">        and ``loss`` fields. See further documentation for details.</span>

<span class="sd">    .. note::</span>
<span class="sd">        To compute log-probabilities for all classes, the ``log_prob``</span>
<span class="sd">        method can be used.</span>

<span class="sd">    Args:</span>
<span class="sd">        in_features (int): Number of features in the input tensor</span>
<span class="sd">        n_classes (int): Number of classes in the dataset</span>
<span class="sd">        cutoffs (Sequence): Cutoffs used to assign targets to their buckets</span>
<span class="sd">        div_value (float, optional): value used as an exponent to compute sizes</span>
<span class="sd">            of the clusters. Default: 4.0</span>
<span class="sd">        head_bias (bool, optional): If ``True``, adds a bias term to the &#39;head&#39; of the</span>
<span class="sd">            adaptive softmax. Default: ``False``</span>

<span class="sd">    Returns:</span>
<span class="sd">        ``NamedTuple`` with ``output`` and ``loss`` fields:</span>
<span class="sd">            * **output** is a Tensor of size ``N`` containing computed target</span>
<span class="sd">              log probabilities for each example</span>
<span class="sd">            * **loss** is a Scalar representing the computed negative</span>
<span class="sd">              log likelihood loss</span>

<span class="sd">    Shape:</span>
<span class="sd">        - input: :math:`(N, \texttt{in\_features})`</span>
<span class="sd">        - target: :math:`(N)` where each value satisfies :math:`0 &lt;= \texttt{target[i]} &lt;= \texttt{n\_classes}`</span>
<span class="sd">        - output1: :math:`(N)`</span>
<span class="sd">        - output2: ``Scalar``</span>


<span class="sd">    .. _Efficient softmax approximation for GPUs:</span>
<span class="sd">        https://arxiv.org/abs/1609.04309</span>

<span class="sd">    .. _Zipf&#39;s law:</span>
<span class="sd">        https://en.wikipedia.org/wiki/Zipf%27s_law</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_features</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">cutoffs</span><span class="p">,</span> <span class="n">div_value</span><span class="o">=</span><span class="mf">4.</span><span class="p">,</span> <span class="n">head_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">AdaptiveLogSoftmaxWithLoss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="n">cutoffs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">cutoffs</span><span class="p">)</span>

        <span class="k">if</span> <span class="p">(</span><span class="n">cutoffs</span> <span class="o">!=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">cutoffs</span><span class="p">))</span> \
                <span class="ow">or</span> <span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">cutoffs</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">)</span> \
                <span class="ow">or</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">cutoffs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">n_classes</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> \
                <span class="ow">or</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">cutoffs</span><span class="p">))</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">cutoffs</span><span class="p">))</span> \
                <span class="ow">or</span> <span class="nb">any</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">!=</span> <span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cutoffs</span><span class="p">]):</span>

            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;cutoffs should be a sequence of unique, positive &quot;</span>
                             <span class="s2">&quot;integers sorted in an increasing order, where &quot;</span>
                             <span class="s2">&quot;each value is between 1 and n_classes-1&quot;</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">in_features</span> <span class="o">=</span> <span class="n">in_features</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span> <span class="o">=</span> <span class="n">cutoffs</span> <span class="o">+</span> <span class="p">[</span><span class="n">n_classes</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">div_value</span> <span class="o">=</span> <span class="n">div_value</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">head_bias</span> <span class="o">=</span> <span class="n">head_bias</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">n_clusters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">head_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_clusters</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">head</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">in_features</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_size</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">head_bias</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">tail</span> <span class="o">=</span> <span class="n">ModuleList</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_clusters</span><span class="p">):</span>

            <span class="n">hsz</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">in_features</span> <span class="o">//</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">div_value</span> <span class="o">**</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)))</span>
            <span class="n">osz</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>

            <span class="n">projection</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">(</span>
                <span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">in_features</span><span class="p">,</span> <span class="n">hsz</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
                <span class="n">Linear</span><span class="p">(</span><span class="n">hsz</span><span class="p">,</span> <span class="n">osz</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
            <span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">tail</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">projection</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">head</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">i2h</span><span class="p">,</span> <span class="n">h2o</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">tail</span><span class="p">:</span>
            <span class="n">i2h</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
            <span class="n">h2o</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">!=</span> <span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Input and target should have the same size &#39;</span>
                               <span class="s1">&#39;in the batch dimension.&#39;</span><span class="p">)</span>

        <span class="n">used_rows</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

        <span class="n">output</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
        <span class="n">gather_inds</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">new_empty</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>

        <span class="n">cutoff_values</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cutoff_values</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>

            <span class="n">low_idx</span> <span class="o">=</span> <span class="n">cutoff_values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
            <span class="n">high_idx</span> <span class="o">=</span> <span class="n">cutoff_values</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>

            <span class="n">target_mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">target</span> <span class="o">&gt;=</span> <span class="n">low_idx</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">target</span> <span class="o">&lt;</span> <span class="n">high_idx</span><span class="p">)</span>
            <span class="n">row_indices</span> <span class="o">=</span> <span class="n">target_mask</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>

            <span class="k">if</span> <span class="n">row_indices</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="k">continue</span>

            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">gather_inds</span><span class="o">.</span><span class="n">index_copy_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">row_indices</span><span class="p">,</span> <span class="n">target</span><span class="p">[</span><span class="n">target_mask</span><span class="p">])</span>

            <span class="k">else</span><span class="p">:</span>
                <span class="n">relative_target</span> <span class="o">=</span> <span class="n">target</span><span class="p">[</span><span class="n">target_mask</span><span class="p">]</span> <span class="o">-</span> <span class="n">low_idx</span>
                <span class="n">input_subset</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">row_indices</span><span class="p">)</span>

                <span class="n">cluster_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tail</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">](</span><span class="n">input_subset</span><span class="p">)</span>
                <span class="n">cluster_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span> <span class="o">+</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span>

                <span class="n">gather_inds</span><span class="o">.</span><span class="n">index_fill_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">row_indices</span><span class="p">,</span> <span class="n">cluster_index</span><span class="p">)</span>

                <span class="n">cluster_logprob</span> <span class="o">=</span> <span class="n">log_softmax</span><span class="p">(</span><span class="n">cluster_output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
                <span class="n">local_logprob</span> <span class="o">=</span> <span class="n">cluster_logprob</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">relative_target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
                <span class="n">output</span><span class="o">.</span><span class="n">index_copy_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">row_indices</span><span class="p">,</span> <span class="n">local_logprob</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>

            <span class="n">used_rows</span> <span class="o">+=</span> <span class="n">row_indices</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>

        <span class="k">if</span> <span class="n">used_rows</span> <span class="o">!=</span> <span class="n">batch_size</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Target values should be in [0, </span><span class="si">{}</span><span class="s2">], &quot;</span>
                               <span class="s2">&quot;but values in range [</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">] &quot;</span>
                               <span class="s2">&quot;were found. &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span>
                                                     <span class="n">target</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
                                                     <span class="n">target</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>

        <span class="n">head_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
        <span class="n">head_logprob</span> <span class="o">=</span> <span class="n">log_softmax</span><span class="p">(</span><span class="n">head_output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">output</span> <span class="o">+=</span> <span class="n">head_logprob</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">gather_inds</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">output</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>

        <span class="k">return</span> <span class="n">_ASMoutput</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_get_full_log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">head_output</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot; Given input tensor, and output of `self.head`,</span>
<span class="sd">        compute the log of the full distribution &quot;&quot;&quot;</span>

        <span class="n">out</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">new_empty</span><span class="p">((</span><span class="n">head_output</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">))</span>
        <span class="n">head_logprob</span> <span class="o">=</span> <span class="n">log_softmax</span><span class="p">(</span><span class="n">head_output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="n">out</span><span class="p">[:,</span> <span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span><span class="p">]</span> <span class="o">=</span> <span class="n">head_logprob</span><span class="p">[:,</span> <span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span><span class="p">]</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">start_idx</span><span class="p">,</span> <span class="n">stop_idx</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cutoffs</span><span class="p">[</span><span class="mi">1</span><span class="p">:])):</span>
            <span class="n">cluster_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tail</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="nb">input</span><span class="p">)</span>
            <span class="n">cluster_logprob</span> <span class="o">=</span> <span class="n">log_softmax</span><span class="p">(</span><span class="n">cluster_output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
            <span class="n">output_logprob</span> <span class="o">=</span> <span class="n">cluster_logprob</span> <span class="o">+</span> <span class="n">head_logprob</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span> <span class="o">+</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>

            <span class="n">out</span><span class="p">[:,</span> <span class="n">start_idx</span><span class="p">:</span><span class="n">stop_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">output_logprob</span>

        <span class="k">return</span> <span class="n">out</span>

<div class="viewcode-block" id="AdaptiveLogSoftmaxWithLoss.log_prob"><a class="viewcode-back" href="../../../../nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob">[docs]</a>    <span class="k">def</span> <span class="nf">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot; Computes log probabilities for all :math:`\texttt{n\_classes}`</span>

<span class="sd">        Args:</span>
<span class="sd">            input (Tensor): a minibatch of examples</span>

<span class="sd">        Returns:</span>
<span class="sd">            log-probabilities of for each class :math:`c`</span>
<span class="sd">            in range :math:`0 &lt;= c &lt;= \texttt{n\_classes}`, where :math:`\texttt{n\_classes}` is a</span>
<span class="sd">            parameter passed to ``AdaptiveLogSoftmaxWithLoss`` constructor.</span>

<span class="sd">        Shape:</span>
<span class="sd">            - Input: :math:`(N, \texttt{in\_features})`</span>
<span class="sd">            - Output: :math:`(N, \texttt{n\_classes})`</span>

<span class="sd">        &quot;&quot;&quot;</span>

        <span class="n">head_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_full_log_prob</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">head_output</span><span class="p">)</span></div>

<div class="viewcode-block" id="AdaptiveLogSoftmaxWithLoss.predict"><a class="viewcode-back" href="../../../../nn.html#torch.nn.AdaptiveLogSoftmaxWithLoss.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot; This is equivalent to `self.log_pob(input).argmax(dim=1)`,</span>
<span class="sd">        but is more efficient in some cases.</span>

<span class="sd">        Args:</span>
<span class="sd">            input (Tensor): a minibatch of examples</span>

<span class="sd">        Returns:</span>
<span class="sd">            output (Tensor): a class with the highest probability for each example</span>

<span class="sd">        Shape:</span>
<span class="sd">            - Input: :math:`(N, \texttt{in\_features})`</span>
<span class="sd">            - Output: :math:`(N)`</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="n">head_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">head_output</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">not_in_shortlist</span> <span class="o">=</span> <span class="p">(</span><span class="n">output</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shortlist_size</span><span class="p">)</span>
        <span class="n">all_in_shortlist</span> <span class="o">=</span> <span class="ow">not</span> <span class="p">(</span><span class="n">not_in_shortlist</span><span class="o">.</span><span class="n">any</span><span class="p">())</span>

        <span class="k">if</span> <span class="n">all_in_shortlist</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">output</span>

        <span class="k">elif</span> <span class="n">not_in_shortlist</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
            <span class="n">log_prob</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_full_log_prob</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">head_output</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">log_prob</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="n">log_prob</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_full_log_prob</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="n">not_in_shortlist</span><span class="p">],</span>
                                               <span class="n">head_output</span><span class="p">[</span><span class="n">not_in_shortlist</span><span class="p">])</span>
            <span class="n">output</span><span class="p">[</span><span class="n">not_in_shortlist</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">log_prob</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">output</span></div></div>
</pre></div>

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